PyTorch GAN playground. Features:
- yaml config file
- Easy interface for Tensorboard
- Generic system - just plug and play
Implemented very simple versions of GAN and ACGAN
.
├── config # Config files (.yml, .json) using with hb-config
├── gan # Generative Adversarial Networks architecture
├── __init__.py # train, evaluate, predict logic
├── module.py # Discriminator, Generator module
└── utils.py # Save and Load Model, TensorBoard
├── acgan # ACGAN architecture
├── __init__.py # train,logic
├── module.py # Discriminator, Generator module
└── utils.py # Save and Load Model, TensorBoard
├── data_loader.py # make_data_loader (using DataLoader)
├── main.py
├── model.py # define Model Spec
└── utils.py # utils
└── plots.py # generic functions to plot you results from a pickle file
└── sampler.py # different sampling methods:multi-modal uniform,muli-modal Gaussian,truncated normal...
Reference : hb-config
- Manage experiments like tf.Estimator
In order to implement your own model just create a new branch, copy the gan directory and edit it as you want.
Pytorch supports many datasets which are implemented to work with the data loaders, so if you want to work on different dataset just add it in the data_loader.py file and repsectively in the config file. Supported data sets
Can control all Experimental environment.
example: config.yml
data:
path: "data/"
model:
z_dim: 100 # Random noise dimension coming into generator, per output vector
real_dim: 784
g_h1: 256
g_h2: 512
g_h3: 1024
d_h1: 1024
d_h2: 512
d_h3: 256
dropout: 0.3
train:
model_dir: "logs/gan"
batch_size: 64
train_steps: 50000
d_learning_rate: 0.0002 # 2e-4
g_learning_rate: 0.0002
optim_betas:
- 0.9
- 0.999
save_checkpoints_steps: 1000
verbose_step_count: 100
predict:
batch_size: 64
slack:
webhook_url: "" # after training notify you using slack-webhookInstall requirements.
pip install -r requirements.txt
Then, start training
python main.py --mode train
After training, generate images
python main.py --mode predict